Enhancing DF-INS for Accurate Zero-Velocity Detection in ILBS: A Dual Foot Synergistic Method
IEEE Sensors Journal, October 2023
Renjie Wu, Boon Giin Lee, Matthew Pike, Linzhen Zhu, Xiaoqing Chai, Yongfu Wang. 2023. Enhancing DF-INS for Accurate Zero-Velocity Detection in ILBS: A Dual Foot Synergistic Method. In IEEE Sensors Journal. DOI:https://doi.org/10.1109/SENSORS56945.2023.10325168
Renjie Wu and Boon Giin Lee and Matthew Pike and Linzhen Zhu and Xiaoqing Chai and Yongfu Wang. (2023). Enhancing DF-INS for Accurate Zero-Velocity Detection in ILBS: A Dual Foot Synergistic Method. IEEE Sensors Journal. https://doi.org/10.1109/SENSORS56945.2023.10325168
Renjie Wu and Boon Giin Lee and Matthew Pike and Linzhen Zhu and Xiaoqing Chai and Yongfu Wang. "Enhancing DF-INS for Accurate Zero-Velocity Detection in ILBS: A Dual Foot Synergistic Method." IEEE Sensors Journal, 2023. https://doi.org/10.1109/SENSORS56945.2023.10325168
Renjie Wu, Boon Giin Lee, Matthew Pike, Linzhen Zhu, Xiaoqing Chai, Yongfu Wang. 2023. Enhancing DF-INS for Accurate Zero-Velocity Detection in ILBS: A Dual Foot Synergistic Method. IEEE Sensors Journal. doi:10.1109/SENSORS56945.2023.10325168
Renjie Wu and Boon Giin Lee and Matthew Pike and Linzhen Zhu and Xiaoqing Chai and Yongfu Wang, "Enhancing DF-INS for Accurate Zero-Velocity Detection in ILBS: A Dual Foot Synergistic Method," IEEE Sensors Journal, 2023. doi: 10.1109/SENSORS56945.2023.10325168
@article{ieee-sensors-2023-1,
title={Enhancing DF-INS for Accurate Zero-Velocity Detection in ILBS: A Dual Foot Synergistic Method},
author={Renjie Wu and Boon Giin Lee and Matthew Pike and Linzhen Zhu and Xiaoqing Chai and Yongfu Wang},
journal={IEEE Sensors Journal},
year={2023},
doi={10.1109/SENSORS56945.2023.10325168}
}
Dual foot inertial navigation system, Zero-velocity detection, Indoor localization, General likelihood ratio test, Moving average filter
Abstract
The Dual Foot Inertial Navigation System (DF-INS) is a promising approach for indoor location-based services (ILBS). Achieving accurate zero-velocity detection is crucial for optimal performance in zero-velocity updating and trajectory calculation. However, conventional techniques rely on fixed thresholds, which are unsuitable for dynamic scenarios and diverse users. This study introduces a dual foot synergistic method to improve zero-velocity detection accuracy. The proposed method smooths the General Likelihood Ratio Test (GLRT) sequences from both feet using a moving average filter and identifies points of equality as transition markers between stance and swing phases. Experimental results on a closed indoor path show that the proposed method outperforms conventional fixed-threshold techniques in zero-velocity detection and DF-INS accuracy. This work contributes to the development of more robust ILBS solutions, particularly for wearable navigation systems.